Introduction

ggplot2 is an R-package, which was developed especially for data visualization. In contrast to many other tools for graphics creation, ggplot2 has its own grammar. This reflects a special philosophy, according to which graphics can be created. This “grammar” is taken from the book The grammar of graphics (Wilkinson 2005). That’s the reason why ggplot2 is called like that: the gg stands for grammar of graphics.

ggplot2 is one of the most used packages in R and has an extremely active community behind it. the ggplot2 mailing list has over 7,000 members and there is a very active Stack Overflow community, with nearly 10,000 questions tagged with ggplot2.

#install.packages("ggplot2")
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.3
citation("ggplot2")
## 
## To cite ggplot2 in publications, please use:
## 
##   H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
##   Springer-Verlag New York, 2016.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Book{,
##     author = {Hadley Wickham},
##     title = {ggplot2: Elegant Graphics for Data Analysis},
##     publisher = {Springer-Verlag New York},
##     year = {2016},
##     isbn = {978-3-319-24277-4},
##     url = {https://ggplot2.tidyverse.org},
##   }

In my opinion, this grammar forces you to think about what you want to say with the graphic. Compared to many other tools where you have to use predefined functions, ggplot2 offers maximum flexibility. The idea behind it is that graphics are put together piece by piece from different modules.

Without predefined functions? Sounds complicated!

In fact, ggplot2 has a set of different core principles that are recurrent or interchangeable. Basically these are:

This workshop will guide you through the basic principles of the package. And you will be able to produce good looking graphics for yourself in the end ;)

Preparation

We will use some further packages in this workshop. ggplot2 is part of the tidyverse and works pretty well together with other packages from it. So we will use the dplyr-package sometimes to prepare the data accordingly.

Furthermore, we will use the gapminder dataset from the gapminder package. And finally we load (or install) the package viridis, which gives us basically another color scheme from the default one, which is good for publications (preserves differences between colors also when you print it in black/white-mode) and for color-blind-people (note: the author sometimes can’t seperate between green and red. Please don’t exclude people like him from exploring your beautiful analyses).

the data

Let’s take a look into the gapminder dataset.

data(gapminder)
gapminder
## # A tibble: 1,704 x 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # ... with 1,694 more rows

As a first step, we extract only the data from the year 2007 from the dataset. We will use the filter-function of dplyr.

gapminder_2007 <- gapminder %>% filter(year==2007)
gapminder_2007
## # A tibble: 142 x 6
##    country     continent  year lifeExp       pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Afghanistan Asia       2007    43.8  31889923      975.
##  2 Albania     Europe     2007    76.4   3600523     5937.
##  3 Algeria     Africa     2007    72.3  33333216     6223.
##  4 Angola      Africa     2007    42.7  12420476     4797.
##  5 Argentina   Americas   2007    75.3  40301927    12779.
##  6 Australia   Oceania    2007    81.2  20434176    34435.
##  7 Austria     Europe     2007    79.8   8199783    36126.
##  8 Bahrain     Asia       2007    75.6    708573    29796.
##  9 Bangladesh  Asia       2007    64.1 150448339     1391.
## 10 Belgium     Europe     2007    79.4  10392226    33693.
## # ... with 132 more rows

the ggplot2-package

geoms

What is meant by modular structure becomes clear here: The basic structure of the graphic is already called by the ggplot command. With the argument aes (aesthetics) you can already define basic options of the graphic. In this case the x-axis should always be the gdp per capita and the y-axis the life expectancy. We pass the data set gapminder_2007 as data.

Thus ggplot already creates the axes. The data is already linked to the graph, but ggplot2 does not yet know the “type” of the graph (the geometric figures).

plot(gapminder_2007)

ggplot(data = gapminder_2007)

ggplot(data = gapminder_2007, aes(x = gdpPercap, y = lifeExp))

geom_point - Scatterplots

The so-called geoms can be used to specify which type of graphic should be displayed. In this case a geom_point as a point graph. ggplot now uses the x and y values and presents them as points.

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp))+
  geom_point()

geom_smooth - Trendlines

Often you want to draw trend lines through point clouds. This can be done in ggplot through the geom_smooth. This puts a regression line (and confidence intervals) through the data. The option method='lm' produces a linear approximation.

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp))+
  geom_point()+
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp))+
  geom_point()+
  geom_smooth(method = "lm")

The ordering of the geoms matter. “Later” geoms, will be added “on top” of the graph.

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp))+
  geom_point(size = 3)+
  geom_smooth(size = 2, method = "loess")

vs.

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp))+
  geom_smooth(size = 2, method = "loess")+
  geom_point(size = 3)

geom_line - Lineplots

By adding another geom - geom_line for line graphs - the modular design of ggplot2-graphs becomes clearer. Values that follow each other on the x-axis are linked together. In this case, of course, connecting the points makes little sense.

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp))+
  geom_point()+
  geom_line()

geom_col or geom_bar - Barplots

pop_by_year <- gapminder %>% group_by(year) %>%  summarise(worldpop = sum(pop, na.rm = TRUE)/1000000000)

ggplot(pop_by_year, aes(x = year, y = worldpop))+
  geom_col()

geom_histogram - histograms

ggplot(gapminder_2007, aes(x = gdpPercap))+
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

geom_density - density plots

ggplot(gapminder_2007, aes(x = gdpPercap))+
  geom_density(fill = "blue", alpha = .3)

geom_boxplot - boxplots

Black Line: Median, Box: 25% and 75%. Half of the distribution inside the box. whiskers: Additional countries Dots: Outliers (out of 95%)

ggplot(gapminder_2007, aes(x = continent, y = lifeExp)) +
  geom_point()

ggplot(gapminder_2007, aes(x = continent, y = lifeExp)) +
  geom_boxplot()

The Cheatsheet for more geoms and more

A fast and good overview over different geoms is the ggplot2-cheatsheet

Additional aesthetics

So far we only had x and y. With aesthetics (aes), however, even more features can be controlled, such as color, groups, dot size, etc. A legend is added automatically.

The following is important: The option color could also be specified outside of aes(). Then you would color all points the same, e.g. in blue. By using it inside the aes() you can specify another variable of the data set and ggplot will use different colors based on this variable: here different colors for different continents.

ggplot(gapminder_2007) +
  geom_point(aes(x = gdpPercap, y = lifeExp, color = continent)) +
  scale_x_log10()

ggplot(gapminder_2007) +
  geom_point(aes(x = gdpPercap, y = lifeExp), color = "blue") +
  scale_x_log10()

Here the point size is set depending on the population size of the country. A second line within the aes does not matter to the R-Code. After commas there is even some nice indention, so you can see to which function the options belong.

ggplot(gapminder_2007, aes(x = gdpPercap, 
                           y = lifeExp, 
                           color = continent, 
                           size = pop)) +
  geom_point() +
  scale_x_log10()

Grouping

by_year_continent <- gapminder %>%
  group_by(year, continent) %>%
  summarize(totalPop = sum(as.numeric(pop)),
            meanLifeExp = mean(lifeExp))

by_year_continent
## # A tibble: 60 x 4
## # Groups:   year [12]
##     year continent   totalPop meanLifeExp
##    <int> <fct>          <dbl>       <dbl>
##  1  1952 Africa     237640501        39.1
##  2  1952 Americas   345152446        53.3
##  3  1952 Asia      1395357351        46.3
##  4  1952 Europe     418120846        64.4
##  5  1952 Oceania     10686006        69.3
##  6  1957 Africa     264837738        41.3
##  7  1957 Americas   386953916        56.0
##  8  1957 Asia      1562780599        49.3
##  9  1957 Europe     437890351        66.7
## 10  1957 Oceania     11941976        70.3
## # ... with 50 more rows

Alright, next plot is a bit weird. Let’s separate the different continents by color. ggplot2 now automatically groups the lines and the plot makes much more sense now.

ggplot(by_year_continent, aes(x = year, y = totalPop)) +
  geom_point() + 
  geom_line() +
  expand_limits(y = 0)+
  scale_color_viridis(discrete = T)

ggplot(by_year_continent, aes(x = year, y = totalPop, color = continent)) +
  geom_point() + 
  geom_line() +
  expand_limits(y = 0)+
  scale_color_viridis(discrete = T)

Some geoms provide the group-option. If we like every line in black. Downside from it: We don’t know, which continent is which.

ggplot(by_year_continent, aes(x = year, y = totalPop, group = continent)) +
  geom_point() + 
  geom_line() +
  expand_limits(y = 0)+
  scale_color_viridis(discrete = T)

Add log scales

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp))+
  geom_point()

Problem: Many countries on the left, with very low gdp percap Possible solution: Log Scale (modular structure - adding a “module” log_scale)

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp))+
  geom_point()+
  scale_x_log10()

ggplot(gapminder_2007, aes(x = pop, y = gdpPercap))+
  geom_point()+
  scale_x_log10()+
  scale_y_log10()

Faceting

Faceting describes a division of the graphic into “subgraphs”. Again, a distinction is made according to the levels in a certain variable (here again continent).

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
  scale_x_log10() +
  facet_wrap(~ continent)

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, size = pop))+
  geom_point()+
  scale_x_log10()+
  facet_wrap(~ year)

including zero

by_year <- gapminder %>%
  group_by(year) %>%
  summarize(totalPop = sum(as.integer(pop)),
            meanLifeExp = mean(lifeExp))

by_year
## # A tibble: 12 x 3
##     year   totalPop meanLifeExp
##    <int>      <dbl>       <dbl>
##  1  1952 2406957150        49.1
##  2  1957 2664404580        51.5
##  3  1962 2899782974        53.6
##  4  1967 3217478384        55.7
##  5  1972 3576977158        57.6
##  6  1977 3930045807        59.6
##  7  1982 4289436840        61.5
##  8  1987 4691477418        63.2
##  9  1992 5110710260        64.2
## 10  1997 5515204472        65.0
## 11  2002 5886977579        65.7
## 12  2007 6251013179        67.0
ggplot(by_year, aes(x = year, y = totalPop/1000000000)) +
  geom_point()

Here the y-axis does not contain the 0 (most often a major mistake). Therefore we have to edit and adjust the scale. Again this can be done by a new “module”.

ggplot(by_year, aes(x = year, y = totalPop/1000000000)) +
  geom_point() +
  expand_limits(y = 0)

Scales

Sometimes it is necessary to change the scale in particular, as with scale_x_log10. But also colors etc. can be changed via scale. There are several functions for changing scales. They all start with `scale_’ and then followed by what you want to change (color, size, etc.)

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point() +
  scale_x_log10() +
  scale_color_discrete(name = "Continent")+
  scale_y_continuous(limits = c(0,100), breaks = seq(0,100,10))

At this point the viridis-package comes in. Some addon-packages to ggplot2 provide their own “scale-options”.

Use the color scales in this package to make plots that are pretty, better represent your data, easier to read by those with colorblindness, and print well in grey scale. Viridis-Package

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d(name = "Continent")+
  scale_y_continuous(limits = c(0,100), breaks = seq(0,100,10))

Basically, aes are linking the data to the graph, while scales decide how the aesthetics will look like.

coords

Sometimes it is necessary to change the orientation of the plot. The only time I use one of these options, is the coord_flip. But there are some more coords-options.

gapminder_2007 %>% top_n(30) %>%  
  ggplot(aes(x = country, y = lifeExp))+
    geom_col()
## Selecting by gdpPercap

gapminder_2007 %>% top_n(30) %>%  
  ggplot(aes(x = country, y = lifeExp))+
    geom_col()+
    coord_flip()
## Selecting by gdpPercap

labels

Of course we also want to label our graphics.

ggplot(gapminder_2007, aes(x = continent, y = gdpPercap)) +
  geom_boxplot() +
  scale_y_log10() + 
  labs(title = "Comparing GDP per capita across continents",
       x = "Continent",
       y = "GDP per capita")

themes

There are some predefined themes in ggplot. In addition, the ggthemes-package contains some more presets. This can make your graphics look like the Wall Street Journal, fivethirtyeight or good old excel.

#install.packages("ggthemes")
library(ggthemes)

All you must do is to add the theme to the ggplot-code-line.

ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point() +
  scale_x_log10()+
  theme_bw()

But of course you can also create your own theme. There are hundreds of different setting options. The most common is probably that you want to remove the legend or change its position, but you can do whatever you like, basically.

ggplot(data = gapminder_2007, aes(x = continent, y = lifeExp, color = continent))+
  geom_boxplot()+
  theme(legend.position = "off")

ggplot(data = gapminder_2007, aes(x = continent, y = lifeExp, color = continent))+
  geom_boxplot()+
  theme(legend.position = "top", 
        axis.text.x = element_text(angle = 90), 
        axis.ticks.length = unit(50, "pt"), 
        panel.background = element_rect(fill = "darkblue", color = "darkblue"))

Addons for fancy stuff

A strength of R are also interactive graphics. When called via ggplotly from the plotly-package, the graphic is already rudimentarily interactive. Labels etc. would have to be edited again. However, some functionability is already available.

#install.packages("plotly")
library(plotly)

p_scatter <- ggplot(gapminder_2007, aes(x = gdpPercap, 
                           y = lifeExp, 
                           color = continent, 
                           size = pop)) +
  geom_point() +
  scale_x_log10()

p_scatter

ggplotly(p_scatter)

The gganimate-package extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.

#install.packages("gganimate")

library(gganimate)

p <- ggplot(gapminder) +
  aes(x = gdpPercap, y = lifeExp, size = pop, color = continent) +
  geom_point() +
  scale_x_log10()+
  guides(color = FALSE, size = FALSE)

p

p + 
  transition_states(year, 1, 0) + 
  ggtitle("{closest_state}")